1) Field of the Disclosure
The disclosure relates to a data driven method and system for predicting operational states of mechanical systems. In particular, the disclosure relates to a data driven method and system for predicting operational states, such as wear or anomalies over time, of mechanical systems.
2) Description of Related Art
Mechanical systems, such as engines, turbines, tires, brakes, and other system components, found in aircraft, automobiles, trucks, watercraft, power generator units, military vehicles, and other vehicles, wear or change over time. Wear affects the performance of such mechanical systems. A key factor in monitoring the health of a mechanical system is to measure the amount of wear to the system as it occurs over time. Such monitoring can aid in maintenance planning and timely repair or replacement of the mechanical system or components of the mechanical system. For example, with gas turbine engines, to get the same thrust output as an engine wears, the engine requires more fuel, and the engine's exhaust gas temperature (EGT), as it leaves the engine, increases. However, EGT is also affected by outside or “nuisance” variables, such as environmental influences (e.g., temperature and air quality), flight conditions, system faults, and other engine parameters for any given flight or data point. Such factors may affect EGT more than wear for a given data point. Typically, engine wear is not evident in a time series plot of raw EGT data plotted over the lifetime of an engine. Thus, EGT by itself may not reveal engine wear that is hidden by the variability due to environmental, operational and other factors.
Known methods and systems exist for monitoring and predicting the wear of a mechanical system. Empirical methods and systems for mechanical system predictions typically manually manipulate recorded data into tables for lookup to predict system wear and anomalies. Such manual empirical methods are limited in the amount of data that can be assembled and are not in an automated format to create a prediction model. In addition, such manual methods may be imprecise because of the outside influences discussed above. Other known methods and systems use theoretical models of the mechanical system which use physics or engineering information to build a model using test data. Such modeling is based on understanding how a system operates and progresses to a failure via knowledge, for example, of material properties and response to loading. However, such physics or engineering model based methods may use simplifying assumptions and are theoretical in nature. Moreover, such methods and systems only collect data when the engine is new and do not continually collect data during flights or track degradation of a system over time. Outside influences, as in the empirical method, are not accounted for.
With regard to known methods and systems that predict mechanical system wear over time, the resulting output must be trended over time due to the imprecision of individual points. The scatter of the individual points is large enough that large rolling averages are required to obtain a value that can be used with confidence. This can cause time delays for any corrective action that may be needed and also for prediction of scheduled maintenance for the engine.
Accordingly, there is a need for a data driven method and system for predicting operational states, such as wear or anomalies over time, of mechanical systems that provide advantages over known methods and systems.